Spontaneous rates exhibit high intra-individual stability across movements involving different biomechanical systems and cognitive demands

Spontaneous rhythmic movements are part of everyday life, e.g., in walking, clapping or music making. Humans perform such spontaneous motor actions at different rates that reflect specific biomechanical constraints of the effector system in use. However, there is some evidence for intra-individual consistency of specific spontaneous rates arguably resulting from common underlying processes. Additionally, individual and contextual factors such as musicianship and circadian rhythms have been suggested to influence spontaneous rates. This study investigated the relative contributions of these factors and provides a comprehensive picture of rates among different spontaneous motor behaviors, i.e., melody production, walking, clapping, tapping with and without sound production, the latter measured online before and in the lab. Participants (n = 60) exhibited high intra-individual stability across tasks. Task-related influences included faster tempi for spontaneous production rates of music and wider ranges of spontaneous motor tempi (SMT) and clapping rates compared to walking and music making rates. Moreover, musicians exhibited slower spontaneous rates across tasks, yet we found no influence of time of day on SMT as measured online in pre-lab sessions. Tapping behavior was similar in pre-lab and in-lab sessions, validating the use of online SMT assessments. Together, the prominent role of individual factors and high stability across domains support the idea that different spontaneous motor behaviors are influenced by common underlying processes.

Variability.The two models concerned with tapping variability as DV again yielded similar null results.Generally, variability of within-session MADMs was substantial (M = 0.08, SD = 0.05).

S4: Pre-processing/Exclusion for different tasks
SMT online assessments: Pre-lab.No influences of browser or device could be detected in the data.Data was removed from analyses for six participants who completed the assessment less than four times.Additionally, three participants were removed due to cleaning criterion 3 and one participant misunderstood the task and provided no taps at all.This left data from a total of 50 participants.
SMT online assessment: In-lab.One participant did not provide any taps, six were removed due to cleaning criterion 1 and 3.This left data from a total of 52 participants.
SMT task.One participant was unable to perform the SMT task in the lab due to technical issues.
Accommodating cleaning criterion 3, three more had to be removed, leaving a total of 55 participants.
SMT with sound.One participant was removed due to cleaning criterion 3; Otherwise, all data could be retained from this task, leaving data from 58 participants.
SPR.One participant experienced technical issues during both melodies.Two participants and one participant stated after the experiment that they were not familiar with the melody of Brother John and Twinkle, respectively.This left data from a total of 56 participants for Brother John and 57 for Twinkle.
Walking and clapping.One clapping audio file was lost due to technical issues.Otherwise, all clapping data could be retained, leaving data from 58 participants.After within-session cleaning, five participants' walking data had to be removed because of too much noise, leaving data from 54 participants.

S6: Audio extraction process
First, the first author went through all audio files and spectrograms to assess time windows to retain in the subsequent analysis in Matlab.It was necessary to remove sections before and after the actual movements as they contained the experimenters' verbal instructions.
Further segments had to be removed for the walking data: In our walking task, participants walked from one end of a room to the other and back.This means that they accelerated and decelerated and stopped once.All these segments of clear acceleration and deceleration (at least the first and last step) and the stopping were thus removed from the audio files.
For the new, clean audio files, we used the mirevents function from the MIRToolbox in Matlab with the mirpeak contrast argument set to 0.15.This argument specifies how local maxima in the spectrogram, i.e., the peaks (in our case onsets: one clap or one step), are defined.A local maximum was thus defined as an onset (or peak) if the difference between it and surrounding (previous and successive) local minima was at least 0.15 times the total amplitude of the entire signal.Though this reliably extracted the peaks for most participants, there were some data files that were simply too noisy.Data files were considered unfit for further analysis if peaks from the audio files were calculated but they did not correspond to real event onsets in the form of claps or steps as evaluated by listening to the audio.An example illustrating this is one participant whose steps were masked by the sounds of the pants' fabric.Thus, no automated onset extraction could be carried out.Four such cases were excluded upon comparing extracted onsets and the respective medians to the audible steps.
We used an audio-based approach to extract walking rate due to the setting of the task.It was conducted in a 7.8m long lab room.Simply calculating steps/min or a similar measure would